// Tencent is pleased to support the open source community by making TNN available.
//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "test/unit_test/layer_test/layer_test.h"
#include "test/unit_test/unit_test_common.h"
#include "test/unit_test/utils/network_helpers.h"
#include "tnn/utils/dims_vector_utils.h"

namespace TNN_NS {

class InstanceNormLayerTest : public LayerTest, public ::testing::WithParamInterface<std::tuple<int, int, int>> {};

INSTANTIATE_TEST_SUITE_P(LayerTest, InstanceNormLayerTest,
                         ::testing::Combine(testing::Values(1, 2), testing::Values(1, 4, 6),
                                            testing::Values(10, 20, 65, 128)));

TEST_P(InstanceNormLayerTest, InstanceNormLayer) {
    // get param
    int batch      = std::get<0>(GetParam());
    int channel    = std::get<1>(GetParam());
    int input_size = std::get<2>(GetParam());

    // blob desc
    auto inputs_desc  = CreateInputBlobsDesc(batch, channel, input_size, 1, DATA_TYPE_FLOAT);
    auto outputs_desc = CreateOutputBlobsDesc(1, DATA_TYPE_FLOAT);

    // param
    LayerParam param;
    param.name = "InstanceNorm";

    // resource
    InstanceNormLayerResource resource;
    int k_count = channel;
    RawBuffer filter_k(k_count * sizeof(float));
    float* k_data = filter_k.force_to<float*>();
    InitRandom(k_data, k_count, 1.0f);
    resource.scale_handle = filter_k;
    RawBuffer bias(k_count * sizeof(float));
    float* bias_data = bias.force_to<float*>();
    InitRandom(bias_data, k_count, 1.0f);
    resource.bias_handle = bias;

    Run(LAYER_INST_BATCH_NORM, &param, &resource, inputs_desc, outputs_desc);
}

}  // namespace TNN_NS
